Model description

This is a Lasso regression model trained on ames housing dataset from OpenML

Intended uses & limitations

This model is not ready to be used in production.

Training Procedure

[More Information Needed]

Hyperparameters

<details> <summary> Click to expand </summary>

Hyperparameter	Value
memory
steps	[('columntransformer', ColumnTransformer(transformers=[('pipeline',<br /> Pipeline(steps=[('standardscaler',<br /> StandardScaler()),<br /> ('simpleimputer',<br /> SimpleImputer(add_indicator=True))]),<br /> <sklearn.compose._column_transformer.make_column_selector object at 0x000001CF5D97B7C0>),<br /> ('onehotencoder',<br /> OneHotEncoder(handle_unknown='ignore'),<br /> <sklearn.compose._column_transformer.make_column_selector object at 0x000001CF128511E0>)])), ('lassocv', LassoCV())]
verbose	False
columntransformer	ColumnTransformer(transformers=[('pipeline',<br /> Pipeline(steps=[('standardscaler',<br /> StandardScaler()),<br /> ('simpleimputer',<br /> SimpleImputer(add_indicator=True))]),<br /> <sklearn.compose._column_transformer.make_column_selector object at 0x000001CF5D97B7C0>),<br /> ('onehotencoder',<br /> OneHotEncoder(handle_unknown='ignore'),<br /> <sklearn.compose._column_transformer.make_column_selector object at 0x000001CF128511E0>)])
lassocv	LassoCV()
columntransformer__n_jobs
columntransformer__remainder	drop
columntransformer__sparse_threshold	0.3
columntransformer__transformer_weights
columntransformer__transformers	[('pipeline', Pipeline(steps=[('standardscaler', StandardScaler()),<br /> ('simpleimputer', SimpleImputer(add_indicator=True))]), <sklearn.compose._column_transformer.make_column_selector object at 0x000001CF5D97B7C0>), ('onehotencoder', OneHotEncoder(handle_unknown='ignore'), <sklearn.compose._column_transformer.make_column_selector object at 0x000001CF128511E0>)]
columntransformer__verbose	False
columntransformer__verbose_feature_names_out	True
columntransformer__pipeline	Pipeline(steps=[('standardscaler', StandardScaler()),<br /> ('simpleimputer', SimpleImputer(add_indicator=True))])
columntransformer__onehotencoder	OneHotEncoder(handle_unknown='ignore')
columntransformer__pipeline__memory
columntransformer__pipeline__steps	[('standardscaler', StandardScaler()), ('simpleimputer', SimpleImputer(add_indicator=True))]
columntransformer__pipeline__verbose	False
columntransformer__pipeline__standardscaler	StandardScaler()
columntransformer__pipeline__simpleimputer	SimpleImputer(add_indicator=True)
columntransformer__pipeline__standardscaler__copy	True
columntransformer__pipeline__standardscaler__with_mean	True
columntransformer__pipeline__standardscaler__with_std	True
columntransformer__pipeline__simpleimputer__add_indicator	True
columntransformer__pipeline__simpleimputer__copy	True
columntransformer__pipeline__simpleimputer__fill_value
columntransformer__pipeline__simpleimputer__keep_empty_features	False
columntransformer__pipeline__simpleimputer__missing_values	nan
columntransformer__pipeline__simpleimputer__strategy	mean
columntransformer__pipeline__simpleimputer__verbose	deprecated
columntransformer__onehotencoder__categories	auto
columntransformer__onehotencoder__drop
columntransformer__onehotencoder__dtype	<class 'numpy.float64'>
columntransformer__onehotencoder__handle_unknown	ignore
columntransformer__onehotencoder__max_categories
columntransformer__onehotencoder__min_frequency
columntransformer__onehotencoder__sparse	deprecated
columntransformer__onehotencoder__sparse_output	True
lassocv__alphas
lassocv__copy_X	True
lassocv__cv
lassocv__eps	0.001
lassocv__fit_intercept	True
lassocv__max_iter	1000
lassocv__n_alphas	100
lassocv__n_jobs
lassocv__positive	False
lassocv__precompute	auto
lassocv__random_state
lassocv__selection	cyclic
lassocv__tol	0.0001
lassocv__verbose	False

</details>

Model Plot

Evaluation Results

Metric	Value
R2 score	0.753308
MAE	0.112742

How to Get Started with the Model

Use the following code to get started:

import joblib
from skops.hub_utils import download
import json
import pandas as pd
download(repo_id="haizad/ames-housing-lasso-predictor", dst='ames-housing-lasso-predictor')
pipeline = joblib.load( "ames-housing-lasso-predictor/model.pkl")
with open("ames-housing-lasso-predictor/config.json") as f:
    config = json.load(f)
pipeline.predict(pd.DataFrame.from_dict(config["sklearn"]["example_input"]))

Model Card Authors

This model card is written by following authors:

[More Information Needed]

Model Card Contact

You can contact the model card authors through following channels: [More Information Needed]

Citation

Below you can find information related to citation.

BibTeX:

[More Information Needed]

Evaluation

evaluation